Journal of Frontiers of Computer Science and Technology ›› 2012, Vol. 6 ›› Issue (12): 1144-1152.DOI: 10.3778/j.issn.1673-9418.2012.12.009

Previous Articles     Next Articles

Mining Spatial Co-location Patterns for Fuzzy Location of Instances

OUYANG Zhiping, WANG Lizhen+, ZHOU Lihua   

  1. Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650091, China
  • Online:2012-12-01 Published:2012-12-03

实例位置模糊的空间co-location模式挖掘研究

欧阳志平,王丽珍+,周丽华   

  1. 云南大学 信息学院 计算机科学与工程系,昆明 650091

Abstract: Fuzzy location of instances can be applied to many areas, such as biomedical image databases, geographic information system (GIS) and more. This paper investigates the spatial co-location patterns mining problem for fuzzy location of instances. Firstly, it defines the related concepts of co-location patterns mining for fuzzy location of instances, including fuzzy location of instances, location participation ratio, etc. Secondly, it proposes a basic algorithm to mine co-location patterns from fuzzy location of instances. Then, it puts forward two kinds of the improved algorithms, grid-based distance calculation and the pruning candidate patterns, so as to improve the mining performance and accelerate the co-location rule generation. Finally, by extensive experiments, this paper verifies the efficiency and effectiveness of the algorithms.

Key words:  spatial data mining, co-location patterns, fuzzy location of instances, location participation ratio

摘要: 实例位置模糊在许多领域里都有着非常重要的应用,比如生物医学图像数据库和地理信息系统(geographic information system,GIS)。研究了实例位置模糊的空间co-location模式挖掘问题。定义了实例位置模糊的空间co-location模式挖掘的相关概念,包括实例位置模糊、位置参与率等;给出了基本算法来挖掘实例位置模糊的co-location模式;提出了两种改进算法,即基于网格的距离计算和减枝候选模式,以提高挖掘性能,加快co-location规则的产生。通过大量的实验,说明了基本算法及其改进算法的效果和效率。

关键词: 空间数据挖掘, co-location模式, 实例位置模糊, 位置参与率